Related papers: AI-driven Uncertainty Quantification & Multi-Physi…
One of the reasons for the lack of understanding of both the mechanisms underlying the HTSC phenomenon and of the instability of materials with Tc > 300 K may be the widely accepted but wrong ideas about the types of chemical bonding in a…
Machine learning (ML) of quantum mechanical properties shows promise for accelerating chemical discovery. For transition metal chemistry where accurate calculations are computationally costly and available training data sets are small, the…
We discuss the possibility of new generation neutrino and astroparticle physics experiments exploiting the liquid Argon Time Projection Chamber (LAr TPC) technique, following a graded strategy that envisions applications with increasing…
The use of transition group metals in electric batteries requires extensive usage of critical elements like lithium, cobalt and nickel, which poses significant environmental challenges. Replacing these metals with redox-active organic…
Superconductor research has traditionally depended on experiments and theoretical approaches. However, the rapid advancement of data-driven methods and machine learning (ML) has opened avenues for accelerating superconductor discovery.…
Concentrated solid solution alloys (CSAs) have attracted attention for their promising properties; however, current manufacturing methods face challenges in complexity, high costs, and limited scalability, raising concerns about industrial…
Magnesium-ion batteries hold promise as future energy storage solution, yet current Mg cathodes are challenged by low voltage and specific capacity. Herein, we present an AI-driven workflow for discovering high-performance Mg cathode…
The half-Heusler compound has drawn attention in a variety of fields as a candidate material for thermoelectric energy conversion and spintronics technology. This is because it has various electronic structures, such as semi-metals,…
Additive manufacturing has enabled the fabrication of advanced reactor geometries, permitting larger, more complex design spaces. Identifying promising configurations within such spaces presents a significant challenge for current…
The temperature dependence of thermodynamic and mechanical properties of six fcc transition metals (Ni, Cu, Ag, Au, Pt, Rh) and the alloying behavior of Ag-Au and Cu-Ni are studied using molecular dynamics (MD). The structures are described…
Accurately simulating coupled physical processes under uncertainty is essential for reliable modeling and design in performance-critical applications such as combustion systems. Ablative heat shield design, as a specific example of this…
Predicting material properties of disordered systems remains a long-standing and formidable challenge in rational materials design. To address this issue, we introduce an automated software framework capable of modeling partial occupation…
Information about the physical properties of astrophysical objects cannot be measured directly but is inferred by interpreting spectroscopic observations in the context of atomic physics calculations. Ratios of emission lines, for example,…
Ultra-high temperature ceramics (UHTCs) represent a class of crystalline materials for extreme environments. They can withstand extremely high temperatures but are mechanically difficult to work with due to their inherent brittleness.…
The optimal design of nanoparticle synthesis protocols is often achieved via one-at-a-time experimental designs. Aside from covering a limited space for the possible input conditions, these methods neglect possible interaction between…
Predicting high temperature superconductors has long been a great challenge. A major difficulty is how to predict the transition temperature Tc of superconductors. Recently, progress in material informatics has led to a number of machine…
The influence of microstructure on thermoelectricity is increasingly recognized. Approaches for microstructural engineering can hence be exploited to enhance thermoelectric performance, particularly through manipulating crystalline defects,…
Extracting reliable information on certain physical properties of materials, like thermal behavior, such as thermal transport, which can be very computationally demanding. Aiming to overcome such difficulties in the particular case of…
Tungsten is considered as one of the most promising materials for nuclear fusion reactor chamber applications. Wire + Arc Additive Manufacturing has already demonstrated the ability to deposit defect-free large-scale tungsten structures,…
The design and performance of future fusion power plants will depend on accurate atomic data for plasma-facing material and plasma impurity species. A leading candidate for the plasma-facing material is tungsten due to its high melting…